OpenRaiser/Intern-Atlas huggingface dataset:
Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition
Venue: arXiv cs.CV 2015
Year: 2015

Motion trajectory recognition is important for characterizing the moving property of an object. The speed and accuracy of trajectory recognition rely on a compact and discriminative feature representation, and the situations of varying rotation, scaling and translation has to be specially considered. In this paper we propose a novel feature extraction method for trajectories. Firstly a trajectory is represented by a proposed point context, which is a rotation-scale-translation (RST) invariant shape descriptor with a flexible tradeoff between computational complexity and discrimination, yet we prove that it is a complete shape descriptor. Secondly, the shape context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space. Experimental results show that, the proposed trajectory feature shows encouraging improvement than state-of-art methods.
A Review of Theory and Practice in Scientometrics
Venue: arXiv cs.DL 2015
Year: 2015

Scientometrics is the study of the quantitative aspects of the process of science as a communication system. It is centrally, but not only, concerned with the analysis of citations in the academic literature. In recent years it has come to play a major role in the measurement and evaluation of research performance. In this review we consider: the historical development of scientometrics, sources of citation data, citation metrics and the "laws" of scientometrics, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments.
Correlation between Channel State and Information Source with Empirical Coordination Constraint
Venue: arXiv cs.IT 2015
Year: 2015

Correlation between channel state and source symbol is under investigation for a joint source-channel coding problem. We investigate simultaneously the lossless transmission of information and the empirical coordination of channel inputs with the symbols of source and states. Empirical coordination is achievable if the sequences of source symbols, channel states, channel inputs and channel outputs are jointly typical for a target joint probability distribution. We characterize the joint distributions that are achievable under lossless decoding constraint. The performance of the coordination is evaluated by an objective function. For example, we determine the minimal distortion between symbols of source and channel inputs for lossless decoding. We show that the correlation source/channel state improves the feasibility of the transmission.
Belief Hidden Markov Model for speech recognition
Venue: arXiv cs.AI 2015
Year: 2015

Speech Recognition searches to predict the spoken words automatically. These systems are known to be very expensive because of using several pre-recorded hours of speech. Hence, building a model that minimizes the cost of the recognizer will be very interesting. In this paper, we present a new approach for recognizing speech based on belief HMMs instead of proba-bilistic HMMs. Experiments shows that our belief recognizer is insensitive to the lack of the data and it can be trained using only one exemplary of each acoustic unit and it gives a good recognition rates. Consequently, using the belief HMM recognizer can greatly minimize the cost of these systems.
The effect of using facebook markup language (fbml) for designing an e-learning model in higher education
Venue: arXiv cs.CY 2015
Year: 2015

This study examines the use of Facebook Markup Language (FBML) to design an e-learning model to facilitate teaching and learning in an academic setting. The qualitative research study presents a case study on how, Facebook is used to support collaborative activities in higher education. We used FBML to design an e-learning model called processes for e-learning resources in the Specialist Learning Resources Diploma (SLRD) program. Two groups drawn from the SLRD program were used; First were the participants in the treatment group and second in the control group. Statistical analysis in the form of a t-test was used to compare the dependent variables between the two groups. The findings show a difference in the mean score between the pre-test and the post-test for the treatment group (achievement, the skill, trends). Our findings suggest that the use of FBML can support collaborative knowledge creation and improved the academic achievement of participatns. The findings are expected to provide insights into promoting the use of Facebook in a learning management system (LMS).
A Model of an E-Learning Web Site for Teaching and Evaluating Online
Venue: arXiv cs.CY 2015
Year: 2015

This research is endeavoring to design an e-learning web site on the internet having the course name as "Object Oriented Programming" (OOP) for the students of level four at Computer Science Department (CSD). This course is to be taught online (through web) and then a programme is to be designed to evaluate students performance electronically while introducing a comparison between online teaching , e-evaluation and traditional methods of evaluation. The research seeks to lay out a futuristic perception that how the future online teaching and e-electronic evaluation should be the matter which highlights the importance of this research.
Joint Channel-and-Data Estimation for Large-MIMO Systems with Low-Precision ADCs
Venue: arXiv cs.IT 2015
Year: 2015

The use of low precision (e.g., 1-3 bits) analog-to-digital convenors (ADCs) in very large multiple-input multiple-output (MIMO) systems is a technique to reduce cost and power consumption. In this context, nevertheless, it has been shown that the training duration is required to be {\em very large} just to obtain an acceptable channel state information (CSI) at the receiver. A possible solution to the quantized MIMO systems is joint channel-and-data (JCD) estimation. This paper first develops an analytical framework for studying the quantized MIMO system using JCD estimation. In particular, we use the Bayes-optimal inference for the JCD estimation and realize this estimator utilizing a recent technique based on approximate message passing. Large-system analysis based on the replica method is then adopted to derive the asymptotic performances of the JCD estimator. Results from simulations confirm our theoretical findings and reveal that the JCD estimator can provide a significant gain over conventional pilot-only schemes in the quantized MIMO system.
Extended Report: Fine-grained Recognition of Abnormal Behaviors for Early Detection of Mild Cognitive Impairment
Venue: arXiv cs.OH 2015
Year: 2015

According to the World Health Organization, the rate of people aged 60 or more is growing faster than any other age group in almost every country, and this trend is not going to change in a near future. Since senior citizens are at high risk of non communicable diseases requiring long-term care, this trend will challenge the sustainability of the entire health system. Pervasive computing can provide innovative methods and tools for early detecting the onset of health issues. In this paper we propose a novel method to detect abnormal behaviors of elderly people living at home. The method relies on medical models, provided by cognitive neuroscience researchers, describing abnormal activity routines that may indicate the onset of early symptoms of mild cognitive impairment. A non-intrusive sensor-based infrastructure acquires low-level data about the interaction of the individual with home appliances and furniture, as well as data from environmental sensors. Based on those data, a novel hybrid statistical-symbolical technique is used to detect the abnormal behaviors of the patient, which are communicated to the medical center. Differently from related works, our method can detect abnormal behaviors at a fine-grained level, thus providing an important tool to support the medical diagnosis. In order to evaluate our method we have developed a prototype of the system and acquired a large dataset of abnormal behaviors carried out in an instrumented smart home. Experimental results show that our technique is able to detect most anomalies while generating a small number of false positives.
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
Venue: arXiv cs.AI 2015
Year: 2015

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). The algorithm extends Lightweight Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random variables for modification to improve convergence of the program output. We show that AdLMH converges to the correct equilibrium distribution and compare convergence of AdLMH to that of LMH on several test problems to highlight different aspects of the adaptation scheme. We observe consistent improvement in convergence on the test problems.
Anima: Adaptive Personalized Software Keyboard
Venue: arXiv cs.HC 2015
Year: 2015

We present a Software Keyboard for smart touchscreen devices that learns its owner's unique dictionary in order to produce personalized typing predictions. The learning process is accelerated by analysing user's past typed communication. Moreover, personal temporal user behaviour is captured and exploited in the prediction engine. Computational and storage issues are addressed by dynamically forgetting words that the user no longer types. A prototype implementation is available at Google Play Store.
Revealing Multiple Layers of Hidden Community Structure in Networks
Venue: arXiv cs.SI 2015
Year: 2015

We introduce a new conception of community structure, which we refer to as hidden community structure. Hidden community structure refers to a specific type of overlapping community structure, in which the detection of weak, but meaningful, communities is hindered by the presence of stronger communities. We present Hidden Community Detection HICODE, an algorithm template that identifies both the strong, dominant community structure as well as the weaker, hidden community structure in networks. HICODE begins by first applying an existing community detection algorithm to a network, and then removing the structure of the detected communities from the network. In this way, the structure of the weaker communities becomes visible. Through application of HICODE, we demonstrate that a wide variety of real networks from different domains contain many communities that, though meaningful, are not detected by any of the popular community detection algorithms that we consider. Additionally, on both real and synthetic networks containing a hidden ground-truth community structure, HICODE uncovers this structure better than any baseline algorithms that we compared against. For example, on a real network of undergraduate students that can be partitioned either by `Dorm' (residence hall) or `Year', we see that HICODE uncovers the weaker `Year' communities with a JCRecall score (a recall-based metric that we define in the text) of over 0.7, while the baseline algorithms achieve scores below 0.2.
A Highly Tunable Virtual Topology Controller
Venue: arXiv cs.NI 2015
Year: 2015

Much research in the last two decades has focused on Virtual Topology Reconfiguration (VTR) problem. However, most of the proposed methods either has low controllability, or the analysis of a control parameter is limited to empirical analysis. In this paper, we present a highly tunable Virtual Topology (VT) controller. First, we analyze the controllability of two previously proposed VTR algorithms: a heuristic method and a neural networks based method. Then we present insights on how to transform these VTR methods to their tunable versions. To benefit from the controllability, an optimality analysis of the control parameter is needed. In the second part of the paper, through a probabilistic analysis we find an optimal parameter for the neural network based method. We validated our analysis through simulations. We propose this highly tunable method as a new VTR algorithm.
Identification of influential nodes in network of networks
Venue: arXiv cs.SI 2015
Year: 2015

The network of networks(NON) research is focused on studying the properties of n interdependent networks which is ubiquitous in the real world. Identifying the influential nodes in the network of networks is theoretical and practical significance. However, it is hard to describe the structure property of the NON based on traditional methods. In this paper, a new method is proposed to identify the influential nodes in the network of networks base on the evidence theory. The proposed method can fuse different kinds of relationship between the network components to constructed a comprehensive similarity network. The nodes which have a big value of similarity are the influential nodes in the NON. The experiment results illustrate that the proposed method is reasonable and significant
Uncertainty in Ontology Matching: A Decision Rule-Based Approach
Venue: arXiv cs.AI 2015
Year: 2015

Considering the high heterogeneity of the ontologies pub-lished on the web, ontology matching is a crucial issue whose aim is to establish links between an entity of a source ontology and one or several entities from a target ontology. Perfectible similarity measures, consid-ered as sources of information, are combined to establish these links. The theory of belief functions is a powerful mathematical tool for combining such uncertain information. In this paper, we introduce a decision pro-cess based on a distance measure to identify the best possible matching entities for a given source entity.
FlexCloud: A Flexible and Extendible Simulator for Performance Evaluation of Virtual Machine Allocation
Venue: arXiv cs.DC 2015
Year: 2015

Cloud Data centers aim to provide reliable, sustainable and scalable services for all kinds of applications. Resource scheduling is one of keys to cloud services. To model and evaluate different scheduling policies and algorithms, we propose FlexCloud, a flexible and scalable simulator that enables users to simulate the process of initializing cloud data centers, allocating virtual machine requests and providing performance evaluation for various scheduling algorithms. FlexCloud can be run on a single computer with JVM to simulate large scale cloud environments with focus on infrastructure as a service; adopts agile design patterns to assure the flexibility and extensibility; models virtual machine migrations which is lack in the existing tools; provides user-friendly interfaces for customized configurations and replaying. Comparing to existing simulators, FlexCloud has combining features for supporting public cloud providers, load-balance and energy-efficiency scheduling. FlexCloud has advantage in computing time and memory consumption to support large-scale simulations. The detailed design of FlexCloud is introduced and performance evaluation is provided.
Signal identification for adaptive spectrum hyperspace access in wireless communications systems
Venue: arXiv cs.NI 2015
Year: 2015

Technologies which will lead to adaptive, intelligent, and aware wireless communications systems are expected to offer solutions to the capacity, interference, and reliability problems of wireless networks. The spectrum sensing feature of cognitive radio (CR) systems is a step forward to better recognize the problems and to achieve efficient spectrum allocation. On the other hand, even though spectrum sensing can constitute a solid base to accomplish the reconfigurability and awareness goals of next generation networks, a new perspective is required to benefit from the whole dimensions of the available electro (or spectrum) hyperspace, beyond frequency and time. Therefore, spectrum sensing should evolve to a more general and comprehensive awareness providing mechanism, not only as part of CR systems but also as a communication environment awareness component of an adaptive spectrum hyperspace access (ASHA) paradigm which can adapt sensing parameters autonomously to ensure robust signal identification, parameter estimation, and interference avoidance. Such an approach will lead to recognition of communication opportunities in different dimensions of the spectrum hyperspace, and provide necessary information about the air interfaces, access techniques and waveforms that are deployed over the monitored spectrum to accomplish ASHA, resource and interference management.
Advances in Human Action Recognition: A Survey
Venue: arXiv cs.CV 2015
Year: 2015

Human action recognition has been an important topic in computer vision due to its many applications such as video surveillance, human machine interaction and video retrieval. One core problem behind these applications is automatically recognizing low-level actions and high-level activities of interest. The former is usually the basis for the latter. This survey gives an overview of the most recent advances in human action recognition during the past several years, following a well-formed taxonomy proposed by a previous survey. From this state-of-the-art survey, researchers can view a panorama of progress in this area for future research.
Cooperative Greedy Pursuit Strategies for Sparse Signal Representation by Partitioning
Venue: arXiv cs.DS 2015
Year: 2015

Cooperative Greedy Pursuit Strategies are considered for approximating a signal partition subjected to a global constraint on sparsity. The approach aims at producing a high quality sparse approximation of the whole signal, using highly coherent redundant dictionaries. The cooperation takes place by ranking the partition units for their sequential stepwise approximation, and is realized by means of i)forward steps for the upgrading of an approximation and/or ii) backward steps for the corresponding downgrading. The advantage of the strategy is illustrated by producing high quality approximations of music signals using redundant trigonometric dictionaries. In addition to rendering stunning improvements in sparsity with respect to the concomitant trigonometric basis, these dictionaries enable a fast implementation of the approach via the Fast Fourier Transform.
Stochastic Analysis of Synchronization in a Supermarket Refrigeration System
Venue: arXiv cs.SY 2015
Year: 2015

Display cases in supermarket systems often exhibit synchronization, in which the expansion valves in the display cases turn on and off at exactly the same time. The study of the influence of switching noise on synchronization in supermarket refrigeration systems is the subject matter of this work. For this purpose, we model it as a hybrid system, for which synchronization corresponds to a periodic trajectory. Subsequently, we investigate the influence of switching noise. We develop a statistical method for computing an intensity function, which measures how often the refrigeration system stays synchronized. By analyzing the intensity, we conclude that the increase in measurement uncertainty yields the decrease at the prevalence of synchronization.
Optimal Design of the Adaptive Normalized Matched Filter Detector
Venue: arXiv cs.IT 2015
Year: 2015

This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the scatter estimates to be greater than a positive regularization parameter rho. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. While an increase of rho seems to improve the conditioning of the estimate, it might however cause it to significantly deviate from the true covariance matrix. The setting of the optimal regularization parameter is a difficult question for which no convincing answers have thus far been provided. This constitutes the major motivation behind our work. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), appropriate when the clutter follows a Gaussian distribution and the regularized Tyler estimator (RTE) for non-Gaussian spherically invariant distributed clutters. Based on recent random matrix theory results studying the asymptotic fluctuations of the statistics of the ANMF detector when the number of samples and their dimension grow together to infinity, we propose a design for the regularization parameter that maximizes the detection probability under constant false alarm rates. Simulation results which support the efficiency of the proposed method are provided in order to illustrate the gain of the proposed optimal design over conventional settings of the regularization parameter.
Development of a Big Data Framework for Connectomic Research
Venue: arXiv cs.DC 2015
Year: 2015

This paper outlines research and development of a new Hadoop-based architecture for distributed processing and analysis of electron microscopy of brains. We show development of a new C++ library for implementation of 3D image analysis techniques, and deployment in a distributed map/reduce framework. We demonstrate our new framework on a subset of the Kasthuri11 dataset from the Open Connectome Project.
A Unified Framework for Identifiability Analysis in Bilinear Inverse Problems with Applications to Subspace and Sparsity Models
Venue: arXiv cs.IT 2015
Year: 2015

Bilinear inverse problems (BIPs), the resolution of two vectors given their image under a bilinear mapping, arise in many applications. Without further constraints, BIPs are usually ill-posed. In practice, properties of natural signals are exploited to solve BIPs. For example, subspace constraints or sparsity constraints are imposed to reduce the search space. These approaches have shown some success in practice. However, there are few results on uniqueness in BIPs. For most BIPs, the fundamental question of under what condition the problem admits a unique solution, is yet to be answered. For example, blind gain and phase calibration (BGPC) is a structured bilinear inverse problem, which arises in many applications, including inverse rendering in computational relighting (albedo estimation with unknown lighting), blind phase and gain calibration in sensor array processing, and multichannel blind deconvolution (MBD). It is interesting to study the uniqueness of such problems. In this paper, we define identifiability of a BIP up to a group of transformations. We derive necessary and sufficient conditions for such identifiability, i.e., the conditions under which the solutions can be uniquely determined up to the transformation group. Applying these results to BGPC, we derive sufficient conditions for unique recovery under several scenarios, including subspace, joint sparsity, and sparsity models. For BGPC with joint sparsity or sparsity constraints, we develop a procedure to compute the relevant transformation groups. We also give necessary conditions in the form of tight lower bounds on sample complexities, and demonstrate the tightness of these bounds by numerical experiments. The results for BGPC not only demonstrate the application of the proposed general framework for identifiability analysis, but are also of interest in their own right.